This invention is related to the field of treatment management and control for patients with chronic care conditions, such as Diabetes, that require long-term control of specific health parameters for the patient.
The invention is explained considering Diabetes, which is a chronic and life-threatening condition. However, it should not be considered as a limitation but as an illustration of application of the invention. Diabetes has no known cure and patients need to control their blood glucose levels by lifestyle changes and/or long-term medication. Diabetes is caused by inadequate availability of or diminished response to an essential body hormone, insulin, affecting energy metabolism and resulting in serious health conditions. There are an estimated 200 million people worldwide suffering from diabetes, with US alone accounting for 18 million patients. The current treatment involves controlling the blood glucose in the appropriate levels by drugs, diet, and exercise regimen. Uncontrolled blood glucose levels cause many complications such as blindness, kidney failure, heart disease, neuropathy, and poor circulation causing amputations. Total costs of Diabetes including costs of associated complications are estimated at $92 billion in US. Primary cause of complications associated with Diabetes treatment is due to high variability in blood glucose control.
With the solutions and techniques available in the market today, a Diabetic patient needs to take insulin response enhancement drugs or take insulin or insulin type drugs to control their blood glucose levels in the appropriate range. There are several internal and external factors that affect a patient's blood glucose levels such as metabolic processes, food intake, physical activities, stress levels etc. So along with medication, patient needs to control these factors closely. With the current available techniques, to manage the blood glucose level within appropriate ranges, a patient needs to understand impact of these factors and manage them on a continual basis, making constant decisions about the impact of these various factors.
In a typical solution, for impact of food intake, a patient needs to understand and estimate carbohydrate count for all types of food and determine the insulin that the patient must administer to keep it in the appropriate range. In another solution, the patient must determine the strength and impact of various activities that the patient may undertake along with carbohydrate counting and understanding glycemic index of the food. Also, the patient must determine the metabolic response and resulting change in blood glucose levels for unit levels of these factors to determine the overall glucose level change. Adding to the complexity the factors may be interdependent and may affect the patient's metabolic response to varying degrees. For example, in different types of illnesses, the estimated impact of a unit level of food, activity metabolic responses may alter and patient needs to account for that to correctly control the blood glucose level. These create complex scenarios for which the patient needs to make estimations introducing errors in estimations and variations in blood glucose control. U.S. Pat. No. 6,691,043 to Rebeiro (2004) discloses a bolus calculator for Diabetic patients to calculate a larger amount of amount of insulin (bolus) when food is ingested or in response to correct blood glucose level higher than prescribed level is observed. However, the patient is making a repetitive estimate about the amount carbohydrates that becomes a critical weak link introducing calculation errors, as patient has no systematic and accurate basis to calculate amount of carbohydrates in any arbitrary or custom food that the patient may ingest. Similarly, U.S. Pat. No. 6,368,272 to Porumbescu (2002) discloses a method to make predictions regarding how a person's biological system will respond to a series of stimuli. It utilizes ongoing information from the user-patient and a time function describing dynamic characteristics of the input to predict ongoing metabolic status. However, here also, the patient makes the estimates about the input information such as food intake, activities etc. and is not provided with a consistent and reliable basis to make such estimates. In addition, use of time functions to create mathematical models in predicting metabolic response and calculation of corrective action such as required amount of Insulin (bolus) creates a much higher level of complexity. This also creates a difficulty in communicating such complex models to the patient. Furthermore, it attempts to improve predictions by improving the mathematical models whereas the input information, on which the mathematical models rely, itself may be erroneous.
In yet another solution, an attempt has been made to create databases of standard types of food giving carbohydrate counts, glycemic index or databases of standard activities giving calories burnt that may somewhat help the patient make better estimates. However, a patient hardly faces standard type of activities or foods in the patient lifestyle. Also even with standardized factor types, the patient still needs to make an estimation of portion or strength of the factor as in strength of standard exercise routine or portion of food. U.S. Pat. No. 7,179,226 to Crothall (2007) discloses a method to compute recommended dosage intake taking into consideration variables such as blood glucose level, activity, meals, etc. It utilizes one or more reference databases such as food database containing USDA food nutrition facts or an activity database containing list of common sports activities and the calorie burn ratios to allow user to select from these common food items or activities and calculate recommended dosage. However, it does not take into account the typical case when patient encounters non-standard factors such as food items or activities. It does not provide a mechanism to the patient to estimate and track such arbitrary, or ad-hoc, or custom factor types. So the issue of patient making erroneous estimates about these arbitrary factor types remains. Not only for arbitrary factor types but also for common factor types, the prior art needs input from the patient about the relative portion of the food item or the relative strength of the activity and that may not be accurate. It does not take into account the fact that the patient input may be erroneous and provides no mechanism to the patient to improve or correct the estimates.
There are solutions being introduced to develop a closed loop feedback system where glucose response can be continuously monitored and any out of control changes can be fed back into a system that will administer medication to bring the blood glucose level back in control. However, there is an inherent latency between body's response to medication or any factor, per say. So any glucose response corrections in such an automated or closed loop system may come later than when needed and also more than needed due to the latency factor, introducing further variations in blood glucose control.
There are several shortcomings in glucose control techniques available today. These create outcome variations and affect treatment effectiveness resulting in risk of serious and costly health complications. Many of the issues that a patient typically encounters with solutions and techniques available today are:
a) A patient is encumbered with making repetitive estimates about the treatment parameters, especially with making estimates about relative strengths of factors such as carbohydrate counts or relative portions of food. This introduces human errors and results in variations in glucose control. Typical factors that patient experiences are arbitrary and non-standard. A patient cannot form a consistent basis to form these estimates accurately. Patient lifestyle is complex and the patient faces mix of input factors that makes it even harder for patient to not only estimate expected change in glucose response but also isolate errors and correct them for a particular factor type. Even smaller mistakes in input parameter estimates create cumulative response variations and over a long-term may have serious impact on patient health.
b) A patient may make mistake not only estimating strength of the input factor but also glucose response sensitivity to input factor. Resulting change in glucose response depends on both strength and sensitivity of input factors. It is hard for the patient to isolate and correct these errors manually. Adding to the complexity is the fact that a patient is not only faced with multiple factors like a mix of physical activities, emotional stress, and ingestion of a few food types, but also that the impact of these factors may be positive or negative, that is augmenting or canceling the errors. So, it becomes a highly complex scenario for a patient to isolate and correct these errors.
c) Each factor type may have a varying impact on the glucose level with different lengths of time. Medications such as Insulin, physical activities such as exercise regime, or oral glucose tablets may be just different types of corrective factors that a patient may need to consider to counteract and keep resulting glucose level under control. So, with a complex mix of factors or with a varying impacts of input factors, a complex counteractive action may be needed by a patient such as different mix of Insulin types or mix of other counteracting factors. With such complex scenarios, patient may not be able to judge and determine the most appropriate corrective action.
d) Since, a patient cannot accurately determine the most appropriate action needed to correct the estimation errors, the patient may make mistakes in corrective steps. This further compounds the problem and complicates correcting the errors.
e) A glucose response is not only affected by specific factors, but also by circumstantial changes such as aging, lifestyle alterations, or sudden unforeseen changes. These changes may be gradual and/or complex for patient to correlate to the response changes manually and hence hard to estimate.
f) Standardized databases of factors such as food, activities etc are insufficient to address arbitrary factors that a patient faces in a practice. Also, even with standardized databases, patient still needs to make estimates about the relative portions or strengths of these factors. With the solutions available today, these errors result into response variations and there is no mechanism for a patient to track, isolate, and address these errors consistently.
g) Static reports that allow a patient to analyze treatment outcomes are insufficient for the patient to isolate the errors. They depict observed response, events, and influencing factors at different time intervals; however they do not track and isolate estimated response for arbitrary factor type. These report fail to accurately correlate individual trackable factor type with the observed response. So, the patient has no consistent way to analyze errors against a particular factor type. Also they neither take into account separately factor type strength and sensitivity nor do they track corrective responses separately. So, the corrective responses can result into further errors, compounding the overall problem.
h) The estimation errors may not only be in total response to a particular factor, but also in responses at different intervals of time. For example a patient's total response to a food type may be accurate but it may be higher than estimated in the beginning and vice a versa. There are no mechanisms available for a patient to isolate and track these errors response for arbitrary food types at different time intervals and may result in significant deviations in response at various time intervals. There are no solutions available that enable patient analyze discrete response measurements at a particular time interval against discrete response measurements of response influencing factors to correct any estimation errors.
Currently, there are no solutions that address the above-mentioned problems and shortcomings.